Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

We demonstrate how incorporating physics constraints into convolutional neural networks
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …

There is a growing interest in developing data-driven subgrid-scale (SGS) models for large-
eddy simulation (LES) using machine learning (ML). In a priori (offline) tests, some recent …

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …

We demonstrate the first climate-scale, numerical ocean simulations improved through
distributed, online inference of Deep Neural Networks (DNN) using SmartSim. SmartSim is a …

The prohibitive cost and low fidelity of experimental data in industry-scale thermofluid
systems limit the usefulness of pure data-driven machine learning methods. Physics …

In this paper, a turbulence model based on deep neural network is developed for turbulent
flow around airfoil at high Reynolds numbers. According to the data got from the Spalart …

In this paper, a novel random forest (RF)-based multifidelity machine learning (ML) algorithm
to predict the high-fidelity Reynolds-averaged Navier-Stokes (RANS) flow field is proposed …

Using various types of data to extract different features and reconstruct flame spontaneous
image, the rapid acquisition of flame propagation data and automatic identification of the …

Robust and accurate Reynolds-averaged stresses and scalar fluxes closure models for
natural convection developed by machine learning are presented in this work. In …